Bridging Zero-Trust Security Architectures with Legacy Clinical Infrastructure: Governance, Trust, and Artificial Intelligence in Contemporary Hospital Cybersecurity

Abstract

The accelerating digital transformation of healthcare systems has intensified longstanding cybersecurity vulnerabilities, particularly those arising from the coexistence of advanced artificial intelligence–enabled applications and deeply entrenched legacy clinical infrastructures. Hospitals increasingly depend on networked clinical workstations, medical devices, and decision-support systems that were designed under perimeter-based security assumptions, yet now operate in threat environments characterized by lateral movement, ransomware, and sophisticated supply-chain attacks. Within this context, zero-trust security architectures have emerged as a dominant paradigm for rethinking trust, access control, and governance in healthcare cybersecurity. This article presents a comprehensive, theoretically grounded, and critically elaborated examination of zero-trust adoption in hospital clinical environments, with particular attention to the challenges posed by legacy operating systems and medical devices. Anchored in recent empirical and evaluative scholarship on Windows 11 adoption in hospital clinical workstations, this study integrates insights from cybersecurity governance, artificial intelligence accountability, blockchain-based trust mechanisms, and healthcare risk management to construct a holistic analytical framework (Nayeem, 2026).

The article advances three core arguments. First, it contends that zero-trust security in healthcare cannot be understood merely as a technical architecture but must be conceptualized as a socio-technical governance model that redefines institutional trust relationships among clinicians, patients, devices, vendors, and regulatory bodies. Second, it demonstrates that legacy systems are not simply technical obstacles to modernization but are embedded within clinical workflows, regulatory compliance regimes, and organizational learning processes, thereby complicating straightforward migration strategies. Third, it argues that artificial intelligence, while frequently positioned as an enabler of zero-trust enforcement and threat detection, simultaneously introduces new accountability, explainability, and ethical challenges that must be addressed through robust governance mechanisms.

Methodologically, the study adopts a qualitative, integrative research design grounded in interpretive analysis of peer-reviewed literature, policy documents, and industry reports. Drawing on established frameworks for systematic and mixed-methods appraisal, the analysis synthesizes diverse strands of scholarship to identify recurring patterns, tensions, and unresolved debates in the literature. The results highlight persistent gaps between zero-trust theoretical models and their practical implementation in healthcare settings, particularly in environments dominated by legacy operating systems and heterogeneous device ecosystems. The discussion extends these findings by situating them within broader debates on digital trust, cyber-resilience, and the future of healthcare information infrastructures.

By offering an extensive theoretical elaboration and critical discussion, this article contributes to scholarly understanding of healthcare cybersecurity governance and provides a foundation for future research on integrating zero-trust principles with legacy clinical systems. The findings underscore the necessity of aligning technical innovation with organizational learning, regulatory adaptation, and ethical accountability to achieve sustainable and trustworthy digital healthcare environments.